{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:SJINACIUVWBQDV6M2FKUJW3S4U","short_pith_number":"pith:SJINACIU","canonical_record":{"source":{"id":"2104.06918","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2021-04-14T15:07:03Z","cross_cats_sorted":[],"title_canon_sha256":"d7f6db56e6f77dc3dc1c93124aebf4469b02123a394e36d314383bb68f2333ae","abstract_canon_sha256":"71cbb0ec61187143656a1c79e021ccd53c0ecafc8f757ec68e68fecd6901d15d"},"schema_version":"1.0"},"canonical_sha256":"9250d00914ad8301d7ccd15544db72e512164890608c73bc9f666cb1dd9133a2","source":{"kind":"arxiv","id":"2104.06918","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2104.06918","created_at":"2026-07-05T02:34:12Z"},{"alias_kind":"arxiv_version","alias_value":"2104.06918v3","created_at":"2026-07-05T02:34:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.06918","created_at":"2026-07-05T02:34:12Z"},{"alias_kind":"pith_short_12","alias_value":"SJINACIUVWBQ","created_at":"2026-07-05T02:34:12Z"},{"alias_kind":"pith_short_16","alias_value":"SJINACIUVWBQDV6M","created_at":"2026-07-05T02:34:12Z"},{"alias_kind":"pith_short_8","alias_value":"SJINACIU","created_at":"2026-07-05T02:34:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:SJINACIUVWBQDV6M2FKUJW3S4U","target":"record","payload":{"canonical_record":{"source":{"id":"2104.06918","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2021-04-14T15:07:03Z","cross_cats_sorted":[],"title_canon_sha256":"d7f6db56e6f77dc3dc1c93124aebf4469b02123a394e36d314383bb68f2333ae","abstract_canon_sha256":"71cbb0ec61187143656a1c79e021ccd53c0ecafc8f757ec68e68fecd6901d15d"},"schema_version":"1.0"},"canonical_sha256":"9250d00914ad8301d7ccd15544db72e512164890608c73bc9f666cb1dd9133a2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:34:12.852789Z","signature_b64":"BJZj0WLLOI9DwzHAwpsNoDOb2DPZDf//pHMLMz62SL2QwYfgMSJtkh2baEtKyhIWGWeON2SMhiqLwrjEEr9JBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9250d00914ad8301d7ccd15544db72e512164890608c73bc9f666cb1dd9133a2","last_reissued_at":"2026-07-05T02:34:12.852364Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:34:12.852364Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2104.06918","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:34:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bmr4rSnBdga5vuakM77cFIYWkg9fQ/JvtJOhap/P5huswSReW/ExhyVhjXCnLqr5WA63T8tYx6isQ8+wjifxDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T17:36:53.031253Z"},"content_sha256":"8dfff51e0d6979d2c5759b5edbc5d830abbebf57a69f853a22f2fff4b225379d","schema_version":"1.0","event_id":"sha256:8dfff51e0d6979d2c5759b5edbc5d830abbebf57a69f853a22f2fff4b225379d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:SJINACIUVWBQDV6M2FKUJW3S4U","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Quantum Convolutional Neural Network on NISQ Devices","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Guilu Long, ShiJie Wei, Yanhu Chen, Zengrong Zhou","submitted_at":"2021-04-14T15:07:03Z","abstract_excerpt":"Quantum machine learning is one of the most promising applications of quantum computing in the Noisy Intermediate-Scale Quantum(NISQ) era. Here we propose a quantum convolutional neural network(QCNN) inspired by convolutional neural networks(CNN), which greatly reduces the computing complexity compared with its classical counterparts, with $O((log_{2}M)^6) $ basic gates and $O(m^2+e)$ variational parameters, where $M$ is the input data size, $m$ is the filter mask size and $e$ is the number of parameters in a Hamiltonian. Our model is robust to certain noise for image recognition tasks and the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.06918","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2104.06918/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:34:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8xxkOMq1T2ODmFupnQXWEJ4Bv4ZxUhTwltVa7LIn+esUczA+uth3DltxjU3DiNTtEgPORbkUFSSuCVOdKg0ZAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T17:36:53.031855Z"},"content_sha256":"05f5c288e617dbb4fc2217f3772c14131238a92ab17bd4bdc9fbe74f19f3f40a","schema_version":"1.0","event_id":"sha256:05f5c288e617dbb4fc2217f3772c14131238a92ab17bd4bdc9fbe74f19f3f40a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SJINACIUVWBQDV6M2FKUJW3S4U/bundle.json","state_url":"https://pith.science/pith/SJINACIUVWBQDV6M2FKUJW3S4U/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SJINACIUVWBQDV6M2FKUJW3S4U/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-19T17:36:53Z","links":{"resolver":"https://pith.science/pith/SJINACIUVWBQDV6M2FKUJW3S4U","bundle":"https://pith.science/pith/SJINACIUVWBQDV6M2FKUJW3S4U/bundle.json","state":"https://pith.science/pith/SJINACIUVWBQDV6M2FKUJW3S4U/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SJINACIUVWBQDV6M2FKUJW3S4U/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:SJINACIUVWBQDV6M2FKUJW3S4U","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"71cbb0ec61187143656a1c79e021ccd53c0ecafc8f757ec68e68fecd6901d15d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2021-04-14T15:07:03Z","title_canon_sha256":"d7f6db56e6f77dc3dc1c93124aebf4469b02123a394e36d314383bb68f2333ae"},"schema_version":"1.0","source":{"id":"2104.06918","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2104.06918","created_at":"2026-07-05T02:34:12Z"},{"alias_kind":"arxiv_version","alias_value":"2104.06918v3","created_at":"2026-07-05T02:34:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.06918","created_at":"2026-07-05T02:34:12Z"},{"alias_kind":"pith_short_12","alias_value":"SJINACIUVWBQ","created_at":"2026-07-05T02:34:12Z"},{"alias_kind":"pith_short_16","alias_value":"SJINACIUVWBQDV6M","created_at":"2026-07-05T02:34:12Z"},{"alias_kind":"pith_short_8","alias_value":"SJINACIU","created_at":"2026-07-05T02:34:12Z"}],"graph_snapshots":[{"event_id":"sha256:05f5c288e617dbb4fc2217f3772c14131238a92ab17bd4bdc9fbe74f19f3f40a","target":"graph","created_at":"2026-07-05T02:34:12Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2104.06918/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Quantum machine learning is one of the most promising applications of quantum computing in the Noisy Intermediate-Scale Quantum(NISQ) era. Here we propose a quantum convolutional neural network(QCNN) inspired by convolutional neural networks(CNN), which greatly reduces the computing complexity compared with its classical counterparts, with $O((log_{2}M)^6) $ basic gates and $O(m^2+e)$ variational parameters, where $M$ is the input data size, $m$ is the filter mask size and $e$ is the number of parameters in a Hamiltonian. Our model is robust to certain noise for image recognition tasks and the","authors_text":"Guilu Long, ShiJie Wei, Yanhu Chen, Zengrong Zhou","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2021-04-14T15:07:03Z","title":"A Quantum Convolutional Neural Network on NISQ Devices"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.06918","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:8dfff51e0d6979d2c5759b5edbc5d830abbebf57a69f853a22f2fff4b225379d","target":"record","created_at":"2026-07-05T02:34:12Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"71cbb0ec61187143656a1c79e021ccd53c0ecafc8f757ec68e68fecd6901d15d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2021-04-14T15:07:03Z","title_canon_sha256":"d7f6db56e6f77dc3dc1c93124aebf4469b02123a394e36d314383bb68f2333ae"},"schema_version":"1.0","source":{"id":"2104.06918","kind":"arxiv","version":3}},"canonical_sha256":"9250d00914ad8301d7ccd15544db72e512164890608c73bc9f666cb1dd9133a2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9250d00914ad8301d7ccd15544db72e512164890608c73bc9f666cb1dd9133a2","first_computed_at":"2026-07-05T02:34:12.852364Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:34:12.852364Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"BJZj0WLLOI9DwzHAwpsNoDOb2DPZDf//pHMLMz62SL2QwYfgMSJtkh2baEtKyhIWGWeON2SMhiqLwrjEEr9JBg==","signature_status":"signed_v1","signed_at":"2026-07-05T02:34:12.852789Z","signed_message":"canonical_sha256_bytes"},"source_id":"2104.06918","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8dfff51e0d6979d2c5759b5edbc5d830abbebf57a69f853a22f2fff4b225379d","sha256:05f5c288e617dbb4fc2217f3772c14131238a92ab17bd4bdc9fbe74f19f3f40a"],"state_sha256":"b534ada737083a400754d619355d1067f079969d64f4a11d53360dba65793823"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CJRb8F3R/cNSNfXZCjio6xhbotet26xMNGJEAHJu8Ar2SuCSoVeZrHaJ3iDIrEbJaM9JzNMoPwHatyyoN4/eDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T17:36:53.036866Z","bundle_sha256":"f2f21a583902d1545a308202600485c83db84d81f38b0f14984b595fcbb97361"}}